CN110489844A - One kind being suitable for the uneven large deformation grade prediction technique of soft rock tunnel - Google Patents
One kind being suitable for the uneven large deformation grade prediction technique of soft rock tunnel Download PDFInfo
- Publication number
- CN110489844A CN110489844A CN201910734981.5A CN201910734981A CN110489844A CN 110489844 A CN110489844 A CN 110489844A CN 201910734981 A CN201910734981 A CN 201910734981A CN 110489844 A CN110489844 A CN 110489844A
- Authority
- CN
- China
- Prior art keywords
- deformation
- grade
- soft rock
- uneven
- rock tunnel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
Abstract
Present disclose provides one kind to be suitable for the uneven large deformation grade prediction technique of soft rock tunnel, obtain geological conditions, execution conditions and the deformation data of each existing soft rock tunnel, it is basic distortion level and the uneven grade of deformation by inhomogeneous deformation grading, to major influence factors classification quantitative, the initial sample database of the uneven large deformation of soft rock tunnel is established；The subjectivity and objective weight of principal element are calculated using different methods, obtain comprehensive weight, each influence factor and soft rock tunnel foundation deformation grade are calculated separately using grey correlation theory and deform the degree of association of uneven grade, the influence factor that relevance is less than setting value carries out reduction；Using the influence factor index after reduction as input parameter, using foundation deformation grade and uneven grade is deformed as output parameter, constructs the uneven large deformation Artificial Neural Network Prediction Model of soft rock tunnel；The acquisition data of soft rock tunnel to be predicted are inputted into Artificial Neural Network Prediction Model, obtain prediction result.
Description
Technical field
The disclosure belongs to ground Deformation Prediction field, is related to a kind of suitable for the uneven large deformation grade forecast of soft rock tunnel
Method.
Background technique
Only there is provided background technical informations relevant to the disclosure for the statement of this part, it is not necessary to so constitute first skill
Art.
Soft rock have intensity is low, porosity is big, degree of consolidation is poor, by construct and weathering influenced it is significant, containing a large amount of dilatancy
The characteristics such as clay mineral generate significant plastic deformation and rheology under engineering construction interference.Due in soft rock structure and intensity
The influence of the factors such as defect and crustal stress, groundwater condition and construction, the Surrounding Rock Strength and stress distribution of soft rock tunnel are poor
It is different and generate inhomogeneous deformation, lead to supporting construction local failure, tunnel safety construction is generated and is seriously affected.Tunnel deformation point
Grade prediction can carry out qualitative prediction and evaluation to non-tunneling cross section deformation, and the prevention and treatment destroyed for tunnel deformation provides foundation.
Understanding according to inventor, traditional tunnel deformation classification prediction mainly uses the methods of empirical equation and numerical simulation,
But since the uneven large deformation of soft rock tunnel is influenced by many factors such as engineering geologic factors and construction factors, belong to non-thread
Property Solve problems, traditional empirical equation and method for numerical simulation have certain limitation, and is difficult to tunnel deformation unevenness
Even degree is predicted.
Summary of the invention
The disclosure to solve the above-mentioned problems, proposes a kind of suitable for the uneven large deformation grade forecast side of soft rock tunnel
Method, the disclosure can carry out Accurate Prediction to the uneven large deformation of soft rock tunnel.
According to some embodiments, the disclosure is adopted the following technical scheme that
One kind being suitable for the uneven large deformation grade prediction technique of soft rock tunnel, comprising the following steps:
Geological conditions, execution conditions and the deformation data for obtaining each existing soft rock tunnel, by inhomogeneous deformation grading
For basic distortion level and uneven grade is deformed, and determines that influence soft rock tunnel is uneven in terms of geologic(al) factor and construction factor
The principal element of even large deformation, and to major influence factors classification quantitative, establish the initial sample of the uneven large deformation of soft rock tunnel
Database；
The subjectivity and objective weight that principal element is calculated using different methods, obtain the comprehensive weight of each factor；
Based on each principal element comprehensive weight, each influence factor and soft rock tunnel are calculated separately using grey correlation theory
Foundation deformation grade and the degree of association for deforming uneven grade, the influence factor that relevance is less than setting value carry out reduction；
It is defeated with foundation deformation grade and the uneven grade of deformation using the influence factor index after reduction as input parameter
Parameter out establishes the sample database of optimization, to construct the uneven large deformation Artificial Neural Network Prediction Model of soft rock tunnel；
The acquisition data of soft rock tunnel to be predicted are inputted into Artificial Neural Network Prediction Model, obtain prediction result.
According to prediction result to prediction belong to deform uneven grade area carry out plus shield construct.
As possible embodiment, geological conditions includes that Rock-mass integrity index, formation dip, rock mass single shaft are full, anti-
Compressive Strength, cohesive strength, deformation modulus, Poisson's ratio, tunnel water water percolating capacity, tunnel first principal stress and edpth of tunnel.
As possible embodiment, execution conditions include tunnel span, excavation method, Tunnel closing time
And supporting intensity.
As possible embodiment, it is averaged relative deformation degree and abnormal large deformation according to tunnel, tunnel is become
Shape is divided into foundation deformation grade and deforms uneven grade, and bigger grade, and inhomogeneous deformation degree is higher；According to numerical values recited or
To the size of deformation influence degree, each influence factor is divided into different brackets；Distortion level and factor grade are assigned
Corresponding classification value, to establish the initial sample database of the uneven large deformation of soft rock tunnel.
The subjective weight of each principal element is calculated by Delphi method as possible embodiment.
The objective weight of each principal element is calculated using rough set theory as possible embodiment.
Index matrix and reference sequences are determined according to initial sample database as possible embodiment, carry out data
Normalization, using the comprehensive weight of each factor as the weighted value of incidence coefficient, calculates each factor using grey correlation theory
The degree of association.
Be input parameter with the influence factor index after reduction as possible embodiment, with foundation deformation grade and
Deforming uneven grade is output parameter, and the data of sample database are normalized, unevenly big to construct soft rock tunnel
Artificial Neural Network Prediction Model is deformed, is predicted using model, is protected using predicted value.
One kind being suitable for the uneven large deformation grade forecast system of soft rock tunnel, comprising:
Sample database constructs module, is configured as obtaining geological conditions, execution conditions and the change of each existing soft rock tunnel
Inhomogeneous deformation grading is basic distortion level by graphic data and deforms uneven grade, and from geologic(al) factor and construction
The principal element for influencing the uneven large deformation of soft rock tunnel is determined in terms of factor, and to major influence factors classification quantitative, is established
The initial sample database of the uneven large deformation of soft rock tunnel；
Weight setting module is configured as being calculated the subjectivity and objective weight of principal element using different methods, be obtained
The comprehensive weight of each factor；
Optimization module is configured as calculating separately respectively based on each principal element comprehensive weight using grey correlation theory
Influence factor and soft rock tunnel foundation deformation grade and the degree of association for deforming uneven grade, the shadow of setting value is less than to relevance
The factor of sound carries out reduction；
Prediction model constructs module, is configured as becoming using the influence factor index after reduction as input parameter with basis
Shape grade and the uneven grade of deformation are output parameter, establish the sample database of optimization, unevenly big to construct soft rock tunnel
Artificial Neural Network Prediction Model is deformed, prediction result is obtained.
A kind of computer readable storage medium, wherein being stored with a plurality of instruction, described instruction is suitable for by terminal device
Reason device, which loads and executes described one kind, is suitable for the uneven large deformation grade prediction technique of soft rock tunnel.
A kind of terminal device, including processor and computer readable storage medium, processor is for realizing each instruction；It calculates
Machine readable storage medium storing program for executing is suitable for being loaded by processor and being executed described one kind and is suitable for for storing a plurality of instruction, described instruction
The uneven large deformation grade prediction technique of soft rock tunnel.
Compared with prior art, the disclosure has the beneficial effect that
1, the disclosure is to rely on a large amount of soft rock tunnel Practical Project, the extensive geological information for collecting Practical Project,
The sample database of construction information and monitoring and measurement data, foundation has range and representativeness.Using Delphi method-rough set reason
It by calculating influence factor comprehensive weight, and is applied in grey relational grade factor reduction, initial sample database is optimized,
Database optimizing method is scientific and reasonable, greatly improves the accuracy of neural network prediction result.Artificial neural network
Prediction technique is distinctive to can solve the characteristics of non-linear arbitrary function approaches, and has unique superiority, and prediction result is accurate
Rate is high.
2, in the uneven Prediction of large deformation of soft rock tunnel, it only will need to need to predict the geological conditions and execution conditions in tunnel
After classification quantitative, as the optimal Artificial Neural Network Prediction Model that input parameter input obtains, the basis in tunnel can be obtained
Distortion level and the uneven grade of deformation, method is simple, reliably.If the foundation deformation grade of prediction is excessive, big according to grade
It is small, it is corresponding to optimize constructing tunnel scheme, foundation supporting intensity is increased, if the uneven grade of deformation of prediction is excessive, according to
Grade size carries out the advance support of respective degrees to abnormal deformation region, this is conducive to preferably control deformation, improves construction
Safety and efficiency.Therefore, the disclosure has great practical value to the design and construction in the uneven large deformation tunnel of soft rock.
Detailed description of the invention
The Figure of description for constituting a part of this disclosure is used to provide further understanding of the disclosure, and the disclosure is shown
Meaning property embodiment and its explanation do not constitute the improper restriction to the disclosure for explaining the disclosure.
Fig. 1 is flow chart of steps；
Specific embodiment:
The disclosure is described further with embodiment with reference to the accompanying drawing.
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the disclosure.Unless another
It indicates, all technical and scientific terms used herein has usual with disclosure person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the disclosure.As used herein, unless the context clearly indicates otherwise, otherwise singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
The disclosure is collecting built and the geological conditions in the soft rock tunnel built, execution conditions and deformation data base extensively
On plinth, in conjunction with Delphi method, rough set theory, grey correlation theory and Artificial Neural Network, propose a kind of suitable
For the uneven large deformation grade prediction technique of soft rock tunnel.The present invention collects geology built and in the soft rock tunnel built extensively
Condition, execution conditions and deformation data, comprehensive analysis soft rock tunnel inhomogeneous deformation feature, are divided into base for inhomogeneous deformation grade
Plinth distortion level and the uneven grade of deformation, and analyzing influence soft rock tunnel is unevenly big in terms of geologic(al) factor and construction factor
The principal element of deformation, and to major influence factors classification quantitative, establish the initial sample data of the uneven large deformation of soft rock tunnel
Library；By Delphi method, the subjective weight of each factor is calculated.It is calculated using initial sample database using rough set theory
Factor objective weight.In conjunction with subjective and objective weight, the comprehensive weight of each factor is calculated；Using initial sample database, in conjunction with it is each because
Plain comprehensive weight calculates separately each influence factor and soft rock tunnel foundation deformation grade and deformation not using grey correlation theory
The relevance of uniform grade carries out reduction to relevance minor impact factor, optimizes to raw sample data library；With about
Influence factor index after letter using foundation deformation grade and deforms uneven grade as output parameter, carrys out structure as input parameter
Build the uneven large deformation Artificial Neural Network Prediction Model of soft rock tunnel.This method with a large amount of soft rock tunnel Practical Project be according to
Support, extensive geological information, construction information and the monitoring and measurement data for collecting Practical Project, the sample database of foundation have wide
Degree and representativeness.Delphi method and rough set theory are combined, subjective and objective weight is comprehensively considered, calculates influence factor synthetic weights
Weight, and be applied in grey relational grade factor reduction, initial sample database is optimized, which greatly improves artificial minds
Accuracy through neural network forecast result.Neural network prediction method is distinctive simultaneously can solve non-linear arbitrary function and forces
Close feature provides possibility for the accurate uneven large deformation problem of soft rock tunnel for solving complexity.This method sample data is true
It is real, informative and representative.Database optimizing method is scientific and reasonable, and prediction technique has unique superiority, in advance
It is high to survey result accuracy rate.There is important directive significance to the prediction and safe construction of the uneven large deformation of soft rock.
As shown in Figure 1, specifically including:
It (1), will be uneven by collecting built and in the soft rock tunnel built geological conditions, execution conditions and deformation data
Deformation extent is classified as foundation deformation grade and deforms uneven grade, and the analyzing influence in terms of geologic(al) factor and construction factor
The principal element of the uneven large deformation of soft rock tunnel, and to major influence factors classification quantitative, it is unevenly big to establish soft rock tunnel
Deform initial sample database.
(2) by Delphi method, the subjective weight of each factor is calculated.Using rough set theory, the objective power of factor is calculated
Weight.In conjunction with subjective and objective weight, the comprehensive weight of each factor is calculated.
(3) each combined factors weight is combined, each influence factor and soft rock tunnel are calculated separately using grey correlation theory
Foundation deformation grade and the degree of association for deforming uneven grade carry out reduction to relevance minor impact factor；
(4) using the influence factor index after reduction as input parameter, with foundation deformation grade and uneven grade is deformed
For output parameter, the sample database of optimization is established, to construct the uneven large deformation neural network prediction mould of soft rock tunnel
Type.
Further, in the step (1), geological conditions includes Rock-mass integrity index, formation dip, and rock mass single shaft is full
With compression strength, cohesive strength, deformation modulus, Poisson's ratio, tunnel water water percolating capacity, tunnel first principal stress, edpth of tunnel.Construction item
Part includes tunnel span, excavation method, Tunnel closing time and supporting intensity.Using conditions above as soft rock tunnel
The major influence factors of uneven large deformation.
Further, it in the step (1), is averaged relative deformation degree and abnormal large deformation according to tunnel, by tunnel
Road deformation is divided into foundation deformation grade and deforms uneven grade, and bigger grade, and inhomogeneous deformation degree is higher.It is big according to numerical value
Size small or to deformation influence degree, is divided into different brackets for each influence factor.It is equal to distortion level and factor grade
Assign corresponding classification value (such as grade be 1, then classification value be 1, grade 2, then classification value be 2, and so on), to establish
The uneven large deformation of soft rock tunnel initial sample database.
Further, in the step (2), Delphi method uses expert graded, and the special of correlative study is engaged in selection
Family respectively gives a mark to the importance of soft rock tunnel foundation deformation grade and the influence factor for deforming uneven grade, passes through Dare
Philippine side method calculates separately each influence factor to the subjective weight of foundation deformation grade and the uneven grade of deformation.Its key step
Are as follows: 1, select expert；2, evaluation opinion consults table design；3, expert consults and discusses the information feedback ask；4, at the data of result
Reason and weight determine.
Further, in the step (2), according to initial sample database, using each influence factor as conditional attribute, respectively
Using foundation deformation grade and uneven grade is deformed as decision attribute, calculates separately each influence factor to base using rough set theory
Plinth distortion level and the objective weight for deforming uneven grade.Its key step: 1, conditional attribute collection and decision kind set are constructed；
2, decision attribute support degree is calculated；3, each conditional attribute is calculated to the significance level of decision attribute；4, weight calculation.
Further, in the step (2), according to the subjective weight and objective weight acquired, formula is utilized:Comprehensive weight is calculated, wherein wiFor the comprehensive weight of i-th of influence factor,It is acquired for rough set method
The objective weight of i-th of influence factor,For the subjective weight for i-th of influence factor that Delphi method acquires, n is to influence
The quantity of factor.
Further, in the step (3), according to initial sample database, using the comprehensive weight of each factor as association
The weighted value of coefficient calculates the degree of association of each factor using grey correlation theory, carries out to relevance minor impact factor
Reduction.Its key step: 1, index matrix and reference sequences are determined；2, data normalization；3, calculate correlation coefficient；4, it calculates and closes
Connection degree.
It further, is input parameter with the influence factor index after reduction, with foundation deformation etc. in the step (4)
Grade and the uneven grade of deformation are output parameter, the sample database of optimization are established, to construct the uneven large deformation of soft rock tunnel
Artificial Neural Network Prediction Model.Its key step: 1, determining input and output parameter, establishes training sample database；
2, data normalization；3, determine artificial neural network basic framework: hidden layer neuron number and the number of plies, neural network type,
Transmission function, training function and learning function etc.；4, the uneven large deformation Artificial Neural Network Prediction Model training of soft rock tunnel,
Obtain optimum prediction model.
It should be understood by those skilled in the art that, embodiment of the disclosure can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the disclosure
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the disclosure, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The disclosure is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present disclosure
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The foregoing is merely preferred embodiment of the present disclosure, are not limited to the disclosure, for the skill of this field
For art personnel, the disclosure can have various modifications and variations.It is all within the spirit and principle of the disclosure, it is made any to repair
Change, equivalent replacement, improvement etc., should be included within the protection scope of the disclosure.
Although above-mentioned be described in conjunction with specific embodiment of the attached drawing to the disclosure, model not is protected to the disclosure
The limitation enclosed, those skilled in the art should understand that, on the basis of the technical solution of the disclosure, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within the protection scope of the disclosure.
Claims (9)
1. one kind is suitable for the uneven large deformation grade prediction technique of soft rock tunnel, it is characterized in that: the following steps are included:
Inhomogeneous deformation grading is base by geological conditions, execution conditions and the deformation data for obtaining each existing soft rock tunnel
Plinth distortion level and the uneven grade of deformation, and determine that influence soft rock tunnel is unevenly big in terms of geologic(al) factor and construction factor
The principal element of deformation, and to major influence factors classification quantitative, establish the initial sample data of the uneven large deformation of soft rock tunnel
Library；
The subjectivity and objective weight that principal element is calculated using different methods, obtain the comprehensive weight of each factor；
Based on each principal element comprehensive weight, each influence factor and soft rock tunnel basis are calculated separately using grey correlation theory
Distortion level and the degree of association for deforming uneven grade, the influence factor that relevance is less than setting value carry out reduction；
Using the influence factor index after reduction as input parameter, with foundation deformation grade and uneven grade is deformed as output ginseng
Number, establishes the sample database of optimization, to construct the uneven large deformation Artificial Neural Network Prediction Model of soft rock tunnel；
The acquisition data of soft rock tunnel to be predicted are inputted into Artificial Neural Network Prediction Model, obtain prediction result.
2. it is as described in claim 1 a kind of suitable for the uneven large deformation grade prediction technique of soft rock tunnel, it is characterized in that: ground
Matter condition include Rock-mass integrity index, formation dip, rock mass single shaft full, compression strength, cohesive strength, deformation modulus, Poisson's ratio,
Tunnel water water percolating capacity, tunnel first principal stress and edpth of tunnel.
3. it is as described in claim 1 a kind of suitable for the uneven large deformation grade prediction technique of soft rock tunnel, it is characterized in that: applying
Work condition includes tunnel span, excavation method, Tunnel closing time and supporting intensity.
4. it is as described in claim 1 a kind of suitable for the uneven large deformation grade prediction technique of soft rock tunnel, it is characterized in that: root
It is averaged relative deformation degree and abnormal large deformation according to tunnel, tunnel deformation is divided into foundation deformation grade and deformation is uneven
Grade, bigger grade, and inhomogeneous deformation degree is higher；According to numerical values recited or to the size of deformation influence degree, by each influence
Factor is divided into different brackets；Corresponding classification value is assigned to distortion level and factor grade, to establish soft rock tunnel
The initial sample database of uneven large deformation.
5. it is as described in claim 1 a kind of suitable for the uneven large deformation grade prediction technique of soft rock tunnel, it is characterized in that: logical
Delphi method is crossed, the subjective weight of each principal element is calculated；Using rough set theory, the objective power of each principal element is calculated
Weight.
6. it is as described in claim 1 a kind of suitable for the uneven large deformation grade prediction technique of soft rock tunnel, it is characterized in that: root
According to initial sample database, determine index matrix and reference sequences, carry out data normalization, using the comprehensive weight of each factor as
The weighted value of incidence coefficient calculates the degree of association of each factor using grey correlation theory.
7. one kind is suitable for the uneven large deformation grade forecast system of soft rock tunnel, it is characterized in that: including:
Sample database constructs module, is configured as obtaining the geological conditions of each existing soft rock tunnel, execution conditions and deformation number
According to being basic distortion level by inhomogeneous deformation grading and deform uneven grade, and from geologic(al) factor and construction factor
Aspect determines the principal element for influencing the uneven large deformation of soft rock tunnel, and to major influence factors classification quantitative, establishes soft rock
The initial sample database of the uneven large deformation in tunnel；
Weight setting module is configured as calculating the subjectivity and objective weight of principal element using different method, obtain it is each because
The comprehensive weight of element；
Optimization module is configured as calculating separately each influence using grey correlation theory based on each principal element comprehensive weight
Factor and soft rock tunnel foundation deformation grade and the degree of association for deforming uneven grade, to relevance be less than the influence of setting value because
Element carries out reduction；
Prediction model constructs module, is configured as using the influence factor index after reduction as input parameter, with foundation deformation etc.
Grade and the uneven grade of deformation are output parameter, the sample database of optimization are established, to construct the uneven large deformation of soft rock tunnel
Artificial Neural Network Prediction Model obtains prediction result.
8. a kind of computer readable storage medium, it is characterized in that: being wherein stored with a plurality of instruction, described instruction is suitable for being set by terminal
Standby processor load and perform claim requires one kind described in any one of 1-6 to be suitable for uneven large deformation of soft rock tunnel etc.
Grade prediction technique.
9. a kind of terminal device it is characterized in that:, including processor and computer readable storage medium, processor is for realizing each finger
It enables；Computer readable storage medium is suitable for for storing a plurality of instruction, described instruction by processor load and perform claim requirement
One kind described in any one of 1-6 is suitable for the uneven large deformation grade prediction technique of soft rock tunnel.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910734981.5A CN110489844B (en) | 2019-08-09 | 2019-08-09 | Prediction method suitable for uneven large deformation grade of soft rock tunnel |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910734981.5A CN110489844B (en) | 2019-08-09 | 2019-08-09 | Prediction method suitable for uneven large deformation grade of soft rock tunnel |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110489844A true CN110489844A (en) | 2019-11-22 |
| CN110489844B CN110489844B (en) | 2021-04-16 |
Family
ID=68549614
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910734981.5A Active CN110489844B (en) | 2019-08-09 | 2019-08-09 | Prediction method suitable for uneven large deformation grade of soft rock tunnel |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110489844B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111914453A (en) * | 2020-07-29 | 2020-11-10 | 中国科学院武汉岩土力学研究所 | Prediction method for large deformation of lamellar soft rock |
| CN115511001A (en) * | 2022-10-21 | 2022-12-23 | 中铁二院工程集团有限责任公司 | Tunnel surrounding rock grading method and device based on air-ground well comprehensive exploration data |
| CN116975623A (en) * | 2023-05-04 | 2023-10-31 | 西南交通大学 | Method, device and medium for predicting large deformation grade in tunnel construction stage by drilling and blasting method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003314188A (en) * | 2002-04-23 | 2003-11-06 | Ohbayashi Corp | Method for predicting settlement in excavation of tunnel |
| CN101737063A (en) * | 2009-11-16 | 2010-06-16 | 西安理工大学 | Ground fissure tunnel asphalt concrete composite lining and supporting method thereof |
| CN103177187A (en) * | 2013-04-01 | 2013-06-26 | 北京市市政工程研究院 | Highway tunnel health status dynamic evaluation method based on variable fuzzy set theory |
| CN107194049A (en) * | 2017-05-09 | 2017-09-22 | 山东大学 | A kind of multi objective Grade system of tunnels and underground engineering rockfall risk |
| CN108005725A (en) * | 2017-12-31 | 2018-05-08 | 上海纽建信息科技有限公司 | A kind of structural healthy monitoring system for Shield Tunnel in Soft Soil |
| CN109359412A (en) * | 2018-11-01 | 2019-02-19 | 山东大学 | The calculation method and system that prediction tunneling shield digging process deforms entirely |
| CN109934398A (en) * | 2019-03-05 | 2019-06-25 | 山东大学 | A kind of drill bursting construction tunnel gas danger classes prediction technique and device |
-
2019
- 2019-08-09 CN CN201910734981.5A patent/CN110489844B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003314188A (en) * | 2002-04-23 | 2003-11-06 | Ohbayashi Corp | Method for predicting settlement in excavation of tunnel |
| CN101737063A (en) * | 2009-11-16 | 2010-06-16 | 西安理工大学 | Ground fissure tunnel asphalt concrete composite lining and supporting method thereof |
| CN103177187A (en) * | 2013-04-01 | 2013-06-26 | 北京市市政工程研究院 | Highway tunnel health status dynamic evaluation method based on variable fuzzy set theory |
| CN107194049A (en) * | 2017-05-09 | 2017-09-22 | 山东大学 | A kind of multi objective Grade system of tunnels and underground engineering rockfall risk |
| CN108005725A (en) * | 2017-12-31 | 2018-05-08 | 上海纽建信息科技有限公司 | A kind of structural healthy monitoring system for Shield Tunnel in Soft Soil |
| CN109359412A (en) * | 2018-11-01 | 2019-02-19 | 山东大学 | The calculation method and system that prediction tunneling shield digging process deforms entirely |
| CN109934398A (en) * | 2019-03-05 | 2019-06-25 | 山东大学 | A kind of drill bursting construction tunnel gas danger classes prediction technique and device |
Non-Patent Citations (1)
| Title |
|---|
| 薛翊国等: "隧道施工期超前地质预报实施方法研究", 《岩土力学》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111914453A (en) * | 2020-07-29 | 2020-11-10 | 中国科学院武汉岩土力学研究所 | Prediction method for large deformation of lamellar soft rock |
| CN111914453B (en) * | 2020-07-29 | 2022-04-12 | 中国科学院武汉岩土力学研究所 | Prediction method for large deformation of lamellar soft rock |
| CN115511001A (en) * | 2022-10-21 | 2022-12-23 | 中铁二院工程集团有限责任公司 | Tunnel surrounding rock grading method and device based on air-ground well comprehensive exploration data |
| CN116975623A (en) * | 2023-05-04 | 2023-10-31 | 西南交通大学 | Method, device and medium for predicting large deformation grade in tunnel construction stage by drilling and blasting method |
| CN116975623B (en) * | 2023-05-04 | 2024-01-30 | 西南交通大学 | Method, device and medium for predicting large deformation grade in tunnel construction stage by drilling and blasting method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110489844B (en) | 2021-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104376420A (en) | Water breakthrough risk evaluation method and evaluation device for water-carrying gas reservoir gas well | |
| CN110489844A (en) | One kind being suitable for the uneven large deformation grade prediction technique of soft rock tunnel | |
| CN104572449A (en) | Automatic test method based on case library | |
| CN105452598B (en) | The method for selecting and optimizing the oil field control for yield platform | |
| CN109934398A (en) | A kind of drill bursting construction tunnel gas danger classes prediction technique and device | |
| CN108416475A (en) | A kind of shale gas production capacity uncertainty prediction technique | |
| CN108573078A (en) | Post-frac effect forecasting method based on data mining | |
| CN107679660A (en) | Based on SVMs by when building energy consumption Forecasting Methodology | |
| CN111178621A (en) | Parameter optimization method of electric heating load prediction support vector regression model | |
| CN109241627A (en) | The dynamic shoring method of probability hierarchical and the device of Automated Design supporting scheme | |
| CN110210648A (en) | Control zone strategy method for predicting based on grey shot and long term memory network | |
| CN109252855B (en) | Method and device for determining final cumulative yield of gas well | |
| CN105528648A (en) | Dynamic production prediction method and device of fracture-cavity unit | |
| CN112801687A (en) | Overhead line engineering-based cost prediction model construction method | |
| CN106327023A (en) | Method and device for measuring transmission project man hour quota | |
| CN105867341A (en) | Online equipment health state self-detection method and system for tobacco processing equipment | |
| Liu et al. | A systems dynamic model of a coal-based city with multiple adaptive scenarios: A case study of Ordos, China | |
| CN107358363A (en) | Coal work incidence of disease Forecasting Methodology based on radial basis function neural network built-up pattern | |
| CN115130375A (en) | Rock burst intensity prediction method | |
| CN113554213A (en) | Natural gas demand prediction method, system, storage medium and equipment | |
| CN110472363A (en) | Surrouding rock deformation grade prediction technique and system suitable for Railway Tunnel | |
| CN110633504A (en) | Prediction method for coal bed gas permeability | |
| CN110988997A (en) | Hydrocarbon source rock three-dimensional space distribution quantitative prediction technology based on machine learning | |
| Aram et al. | Qualitative and quantitative cost estimation: a methodology analysis | |
| CN107093018A (en) | Communication engineering project information method for visualizing and device based on health model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |